Quantum Artificial Intelligence

The cross-integration of quantum computing and artificial intelligence promotes the continuous development of quantum artificial intelligence. This book aims to build quantum artificial intelligence applications in a way that is friendly to deep learning enthusiasts. The book has 13 chapters in total. Chapter 1 and Chapter 2 systematically introduce the development history of quantum computers and the basic knowledge of quantum computing programming. Chapters 3 to 7 respectively introduce different deep learning methods and the way to construct quantum-inspired algorithms based on the logic of these algorithms. The phase in the quantum circuit is used as the learnable parameter of the neural network and reconstructed into a quantum neural network operator. These operators can be called directly in the PyTorch environment. Chapters 8 and 9 are advanced knowledge of quantum artificial intelligence, explaining the feasible evaluation methods of different quantum algorithms and the operatorization of quantum neural networks based on Torch. Script technology. Chapters 10 to 13 introduce quantum algorithms into the native deep learning PyTorch environment to bring about possible quantum enhancements, and respectively implement cases such as the search for the phase change process of material crystal structure, the prediction of coronavirus RNA sequence variation, the prediction of affinity energy in drug virtual screening, and the design of gene expression drugs. This book can be used as an introductory book for beginners of quantum artificial intelligence, a reference book for PyTorch deep learning enthusiasts, and an application guide for technicians engaged in quantum artificial intelligence related work.